Technique For Generating Near Real-Time Transport Modality Statistics

ABSTRACT

Methods and apparatuses for generating real-time or near-real-time transport modality statistics by correlating mobile network and public transport location data are provided. In an example, a processing node may perform a method using one or more processing circuits, the method including identifying paths traveled by multiple vehicles and determining paths traveled by multiple subscriber telecommunications devices of one or more network or service providers. In addition, the method may include correlating the paths traveled by the multiple vehicles and the paths traveled by the multiple subscriber telecommunications devices to determine which one or more modes of transportation each subscriber telecommunications device used over the path traveled by the subscriber telecommunications device. Corresponding apparatus, computer programs, and signals are also described.

TECHNICAL FIELD

The present application generally relates to wireless subscriberlocation analysis, and particularly relates to generating transportmodality statistics for wireless subscribers by correlating mobilesubscriber telecommunication device and vehicle location data.

BACKGROUND

In order to efficiently plan and optimize public transport systems inbig cities, a vast amount of user-specific mobility data is required.Such data has traditionally been collected by surveying a smallpercentage of households and by manually counting passengers on eachtransportation line or vehicle. This remains the most used approachtoday in most cities.

Recently, a number of information technology solutions have emerged. Forinstance, electronic ticketing systems, which are being deployed in moreand more cities (e.g., New York, Tokyo, Shanghai, etc.), providedetailed and accurate information about public transport usage. Severalsolutions built on mobility information from mobile networks are indevelopment, including some that build origin-destination matrices frommobile network data and then utilize these matrices to optimize publictransport planning and usage. Smartphones are also aware of user (alsoreferred to herein as “subscriber”) location from global positioningsystem (GPS) and/or mobile network cell information, and as such, majorservice providers and platform vendors possess large mobility data setsfrom which mobility statistics can be derived.

There are downsides, however, to each of these existing solutions.Traditional household surveys and manual passenger counting are eachexpensive (both monetarily and in terms of human work involved) andquite inaccurate (only cover a very small subset of citizens and timeperiods). Electronic ticketing systems, though providing detailedpassenger use data, are expensive to build and do not provide anyinformation about other modes of transport (also referred to as“modality” or “modalities”) such as walking, bicycles, cars, etc. Also,some of these systems are “check-in only” to simplify usage, i.e., datais logged only when boarding a vehicle. These “check-in only” systemsprovide much less accurate statistics than the ones where check-out isalso mandatory.

With the current high mobile phone usage rates, telecom data can covermobility patterns of practically all citizens. However, all of thecurrently published systems are using circuit-switched call data records(CDRs) only, which contain location information only in point in timeswhen a user is making voice calls or sending or receiving text ormultimedia messages. These current methods provide infrequent andincomplete mobility information, requiring long time periods (i.e.,several weeks or months) to render reliable origin-destination matrixestimates. Hence, these existing methods can provide only long-termaverage traffic characteristics not suitable to identify shorter term(second- or minute-wise, i.e., near-real-time, hourly, daily) dynamicchanges in mobility patterns due to road works, line reconstructions,replacement services, events, and the like. Finally, modalityinformation (i.e., transport means being used) is not possible toextract using the currently used data.

Accordingly, improved techniques for providing user mobility data andutilizing that data to generate user modality information are needed.

SUMMARY

The present disclosure describes example methods and apparatuses forgenerating real-time or near-real-time transport modality statistics bycorrelating mobile network and public transport location data. In anexample method, a processing node may, using one or more processingapparatuses, identify paths traveled by multiple vehicles and determinepaths traveled by multiple subscriber telecommunications devices of oneor more network or service providers. In addition, the processing nodemay utilize the one or more processing apparatuses to correlate thepaths traveled by the multiple vehicles and the paths traveled by themultiple subscriber telecommunications devices to determine which one ormore modes of transportation each subscriber telecommunications deviceused over the path traveled by the subscriber telecommunications device.

Corresponding apparatus, computer programs, and signals are alsodescribed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system implementing aspects of one ormore embodiments described herein.

FIG. 2 is a block diagram of a system implementing aspects of one ormore embodiments described herein.

FIG. 3 is a logic flow diagram of a method implemented by a processingnode according to one or more embodiments.

FIG. 4A is a block diagram of a processing node according to one or moreembodiments.

FIG. 4B is a block diagram illustrating example aspects of processingcircuits of a processing node according to one or more embodiments.

FIG. 5 is a block diagram illustrating example aspects of a processingnode according to one or more embodiments.

FIGS. 6a to 6d are diagrams illustrating test results of exemplary testsperformed using the technique of the present disclosure.

FIG. 7 illustrates a multi-layer anonymization schema for protectingprivacy of individuals when using the technique of the presentdisclosure.

FIG. 8 schematically illustrates an approach for processing thetechnique of the present disclosure utilizing scaling options in a cloudenvironment.

DETAILED DESCRIPTION

Embodiments presented herein solve the problems of the presenttechniques by generating passenger paths tagged with transport modalityin a given city in near real-time by correlating position and timeinformation of subscriber-associated wireless telecommunications devices(e.g., user equipment (UE)) with position and time information of one ormore vehicles. Thus, the position and time information from which thecorrelation may be garnered may include location information ofsubscribers gathered from mobile telecommunication networks orover-the-top (OTT) service providers and positioning information ofpublic transport vehicles.

Some embodiments described herein employ a stochastic mathematicalmethod on the position and time information in order to determine thepublic transport routes and vehicles the subscriber associated with awireless communication device takes. The applied method generates aprobabilistic distribution function of positions of the multiplesubscriber telecommunications devices for each telecommunication celland/or each telecommunication cell pairs between which cell changesoccur, and based on heuristics, yields the public transport vehicles(e.g., specific bus service, subway train, etc.) that any given mobilesubscriber is riding with the highest probability. If the subscriber ismost probably not riding any public transport vehicles, then otherheuristics are used to assign another modality such as walking, bicycleor car. Thus, by utilizing near-real-time position and location dataassociated with one or more wireless telecommunication devices andvehicles, detailed and continuously updated statistics can be obtainedabout actual traffic demand and actual public transport vehicleutilization. This detailed information can in turn be used to optimizethe public transport system of a city or an agglomeration.

FIG. 1 illustrates a communication system 10 according to one or moreembodiments of the present disclosure. As shown in the Figure, thecommunication system 10 includes a processing node 106, which mayreceive position, position accuracy, position timestamp and otherlocation-related information 108 from one or more of several sources.These sources may include one or more wireless communication networks orOTT service provider networks tracking the geographical location ofwireless telecommunication devices and one or more public transportsystems tracking the geographical location of vehicles. Otherlocation-related information may include but is not limited totelecommunication cell identifiers and cell change events. In an aspect,from the received position and time information 108, the processing node106 may correlate one or more vehicle paths 104 (including 104A and104B) with multiple paths of subscriber telecommunication devices 102(including 102A and 102B). The system 10 may include one or morewireless communication networks, systems, sub-systems, or the like, aswell as one or more wireline communication networks, systems,sub-systems, or the like, or those that have both wireless and wirelinecommunication functionality.

In the specific embodiment shown in FIG. 1, multiple vehicles 101,including a bus 101A and a subway train 1016, travel through ageographical area 105 along multiple paths 104 (i.e., 104A and 104B,respectively) corresponding to each of the multiple vehicles 101.Likewise, one or more wireless telecommunication devices 102, including102A and 102B, may follow corresponding paths 103A and 103B through thegeographic region 105. Position and time information 108 for the pathsof the one or more subscriber telecommunications devices and thevehicles are obtained by the processing node 106 through one or moretechniques disclosed in greater detail below.

In an aspect of the present disclosure, the processing node 106 canidentify one or multiple vehicle paths 104, for instance, based on theobtained position and time information 108. Likewise, the processingnode 106 can determine the one or multiple paths 103 traveled bymultiple subscriber telecommunications devices 102 having one or morecorresponding network or service providers. After identifying anddetermining these paths 103 and 104, the processing node 106 cancorrelate the paths 104 traveled by the multiple vehicles and the paths102 traveled by the multiple subscriber telecommunications devices 102to determine (e.g., estimate) which one or more modes of transportation(e.g., walking, bus, subway train, etc.) each subscribertelecommunications device 102 used over the path 103 traveled by thesubscriber telecommunications device 102.

In at least some embodiments, the processing node 106 and subscribertelecommunications device 102 operate in wireless communication systems10 that comprise an Evolved UMTS Terrestrial Radio Access Network(E-UTRAN) and widely known as the Long-Term Evolution (LTE) system, orin a 5G communication system, for example. However, it will beappreciated that the techniques may be applied to other wirelessnetworks, as well as to successors of the E-UTRAN. Thus, referencesherein to signals using terminology from the 3GPP standards for LTEshould be understood to apply more generally to signals having similarcharacteristics and/or purposes, in other networks.

A processing node 106 herein is any type of processing device capable ofcommunicating with another node (i.e., other processing device) viawired communication or over the air via wireless communication usingradio signals. In example embodiments, the processing node 106 can be adevice in any network, such as but not limited to communication system10. This may include devices in an access network and/or a core network,packet-switched networks and/or circuit switched networks, a public orprivate cloud and/or any other network type generally known in the art.

Subscriber telecommunications device 102 is any type device capable ofcommunicating with a network using wired or wireless signals. Thesedevices may include mobile telephones, smartphones, tablets, e-readers,laptops, wearables, or any other device that is generally mobile andconfigured to communicate via a wireless access network. As thesubscriber telecommunications device 102 may be a user equipment, or“UE,” in some examples, for purposes of the present disclosure, thesubscriber telecommunications device 102 of FIG. 1 may be referred toherein as a UE.

A subscriber telecommunications device 102 may also be referred to as aradio device, a radio communication device, a wireless terminal, orsimply a terminal. Unless the context indicates otherwise, the use ofany of these terms is intended to include device-to-device UEs ordevices, machine-type devices or devices capable of machine-to-machinecommunication, sensors equipped with a wireless device, wireless-enabledtable computers, mobile terminals, smart phones, laptop-embeddedequipped (LEE), laptop-mounted equipment (LME), USB dongles, wirelesscustomer-premises equipment (CPE), etc. In the discussion herein, theterms machine-to-machine (M2M) device, machine-type communication (MTC)device, wireless sensor, and/or sensor, or any other device that may beutilized in an Internet-of-Things (IOT) system. Particular examples ofsuch machines are power meters, industrial machinery, or home orpersonal appliances, e.g., refrigerators, televisions, personalwearables such as watches etc. In other scenarios, a wirelesscommunication device as described herein may be comprised in a vehicleand may perform monitoring and/or reporting of the vehicle's operationalstatus or other functions associated with the vehicle.

In addition, one or more of the subscriber telecommunications devices102 can each be associated with a particular “subscriber” (may also bereferred to as a “user” herein). In a non-limiting aspect, thissubscriber is generally any registered and/or identifiable unit (humanor otherwise) permitted to utilize a particular network (access network,core network, data network) and/or service provider (e.g., Netflix,Google, Amazon, and the like). The subscriber may be a device owner(human, company, company employee), mobile service consumer (Facebookuser/account), or object, i.e., any physical or abstract “thing” that iscapable of transport, such as a consumer product in the supply chain, acouriered document, a tour company tour group, item owned by aparticular company, an employee, or generally any living or non-livingentity that can correspond to a subscriber telecommunications device 102whose path is tracked and identified by processing node 106. Forpurposes of the present disclosure, though the term “subscribertelecommunications device” is used to describe a mobile station, UE,and/or any mobile device carried in relatively close proximity to asubscriber, the term “subscriber” can also be used to refer to thesubscriber telecommunications device 102 corresponding to thesubscriber. In other words, as both the subscriber telecommunicationsdevice 102 and its corresponding subscriber(s) are likely to travelessentially the same path (i.e., the subscriber telecommunicationsdevice 102 maintains a relatively close proximity to the subscriber),the term “subscriber” herein can likewise optionally refer to thecorresponding subscriber telecommunications device 102.

Ultimately, the processing node 106 identifies the paths travelled byone or multiple vehicles and determines paths travelled by one ormultiple subscriber telecommunications devices 102 based on position andtime data 108 obtained by the processing node 106 via at least one ofseveral possible data sources discussed further below. Once the vehicleand subscriber paths have been obtained, the processing node 106 appliesa stochastic mathematical method on the data in order to determine thelikely vehicles of the one or multiple vehicles (for which paths wereobtained) utilized by the one or more subscriber telecommunicationsdevices 102 on their corresponding paths. The processing node 106likewise builds a probabilistic distribution function of positions ofthe multiple subscriber telecommunications devices for eachtelecommunication cell and/or each telecommunication cell pairs betweenwhich cell changes occur based on the position and time data 108.Thereafter, the processing node 106 yields one or multiple vehicles(e.g., specific bus service, subway train, etc.) that any givensubscriber telecommunications device 102 utilized along itscorresponding path with a particular probability and/or degree ofconfidence, which may be relative (i.e., with the highest probability)or absolute (i.e., measured against a preconfigured or dynamic thresholdvalue). If the processing node 106 determines, on the other hand, that aparticular one of the subscriber telecommunications devices 102 likelydid not utilize any public transport vehicles (again, can be in relativeor absolute terms), other heuristics are used to assign anon-public-vehicle modality to the path or portion thereof (such aswalking, bicycling, and/or using a car, for example). Ultimately, aftermultiple days (and/or multiple path iterations for the one or multiplesubscriber telecommunications devices 102 and/or one or multiplevehicles 101), stored likelihood time series' and mapping results can beanalyzed by the processing node 106 to render a long-term subscriberprofile for the one or multiple subscribers.

FIG. 2 illustrates further aspects of the system 10 presented above inrelation to FIG. 1, with particular detail provided for an exampleprocessing node 106 and the functionality thereof. In an aspect ofexample embodiments, the processing node 106 can receive several typesof position and timing information 108 from several sources, each ofwhich provides a particular advantage and unique insight into thesubscriber-vehicle correlation process. Thus, one key feature that makesthe present improvements possible is the availability of relativelyfine-grained (e.g., at the cell or sub-cell level) mobile positioninginformation to subscribers, as well as GPS position and time informationfor public transport vehicles. These specific and non-limiting forms ofposition and time information 108 and the location data sources 204providing this information will now be discussed.

First, as shown in FIG. 2, processing node 106 can receive vehiclelocation information 205 obtained and managed by a vehicle system, suchas, but not limited to, a public transport vehicle system 201 thatincludes one more multiple vehicles, including 101A and/or 101B. In someexamples, rather than a public transport vehicle system 201, the vehiclesystem may be a private vehicle system such as a taxi or automobileridesharing fleet, bikeshare system, or the like. With respect toobtaining the vehicle location information 205 at the vehicle system201, an increasing number of municipalities in the United States (andindeed the world) have real time public transport vehicle trackingsystems whose data is generally available via one or morepublicly-available Application Program Interfaces (APIs), such as thestandard General Transit Feed Specification (GTFS)-realtime interface(or similar interface). In an aspect, this position and timinginformation for the vehicles in the vehicle system should be obtained toensure that it is as accurate as possible, and therefore GPS-basedsystems are ideal though not required. In addition, the vehicle locationinformation 205 is optimally provided with fine time granularity and isavailable for a maximum number of public transport vehicles, in thepublic transport vehicle system 201.

In addition to vehicle location information 205, the processing node 106can also be provided with position and timing information 108 from oneor more mobile networks and/or service providers 202 in the form ofsubscriber locations 206. Mobile network footprints are increasingthroughout the world, providing further coverage reach for mobilepositioning systems and providing increasingly more complete andaccurate position and time information for subscriber telecommunicationsdevices 102 (or “localization information”), e.g., densifying cellcoverage improves location accuracy. For one, these modern mobilecommunication systems generate and expose position and time informationmore frequently than the legacy wireless communication systems (whichimplement call data record (CDR)-based techniques that limitedgeneration of position and time information when establishing a phonecall or sending/receiving user data communications (such as a text ormedia message)). Though modern wireless systems still require subscribercommunication devices 102 to be active in order to generate and/orupdate position and time information, continuously increasing smartphoneusage or penetration (e.g., frequency of location updates iscontinuously improving) and many applications performing regularbackground data transfers ensure frequent network activity for mostusers, which in turn increases position and timing informationgeneration opportunities. Also, the Radio Access Network (RAN) of modernmobile telecommunication systems can be configured to generate detailedreports of any such network activities that can be used to estimate alsosub-cell location, for example. This shortened effective intervalbetween data generation triggering events provides more granularposition and timing information to processing node 106, and thus arelatively faster generation rate for accurate localization information.

The subscriber locations 206 provided to processing node 106 may includethe subscriber locations 206 in terms of a cell or sub-cell location,and as such, may provide less accurate and/or less precise informationthan locations provided by systems that use GPS (such as those providingthe vehicle location information 205 for the vehicles). As such, toimprove accuracy and precision with respect to the position and timeinformation 108 of the subscriber telecommunications devices 102, if aparticular vehicle system and/or public transport company has anavailable smartphone application (app) or the mobile network operatorhas performed drive tests in its network, GPS location data from thispublic transport app and/or the drive tests 203 and/or from any othersources can be further correlated with the cell- or sub-cell-basedsubscriber locations 206 to improve overall location accuracy in system10. Based on this correlation, geographical location distributionstatistics 207 can be generated for each cell, cell change relation orany other network-related location identifier and sent along to theprocessing node 106.

As shown in FIG. 2, the processing node 106 can use these geographicallocation distribution statistics 207 (and/or the raw GPS/cell locationinformation in some instances) to form a geographical locationdistribution 209 for each cell, cell-change relation or any othernetwork-related location identifier of the given network(s). Thegeographical location distribution 209 can be maintained in a database(at the processing node 106 or another device) that is built and updatedcontinuously by processing node 106. The geographical locationdistribution 209 yields a probability distribution function of theposition of a subscriber telecommunications device 102 given themeasured position in a particular network. As such, the probabilitydistribution function can incorporate the position and time information108, including a geographical area and/or any temporal parameter, e.g.,time of day, day of week, etc., in order to reflect the heterogeneousaccuracy level of the various spatio-temporal measurements provided bythe location data sources 204.

In addition to building and maintaining the geographical locationdistribution 209, the processing node 106 can include a component ormodule that calculates, based on the vehicle location information 205and subscriber locations 206, the likelihood that a given subscribertelecommunications device 102 is riding a given vehicle (or utilizing aparticular mode of transport), e.g., on a particular route segment, at agiven point in time. Whenever subscriber position and time information108 is available from the mobile network and/or service provider for agiven subscriber, the likelihood analysis component or module 208 candetermine the position of all nearby vehicles (including publictransport vehicles, in some examples) by interpolating stored locationand time information for the same timestamp, and a probability of thegiven subscriber telecommunications device 102 being at each knownvehicle location is calculated based on the geographical locationdistribution database discussed above.

In addition, based on a likelihood time series 210 for specific mobilesubscriber and vehicle combinations and public transport line data (suchas stop locations for specific lines), maximum likelihood methods can beused by one or more processing components or modules of the processingnode 106 to identify the most probable routes, paths, public transportline segments, etc. used by each subscriber. In other words, processingnode 106 includes a component or module for performingsubscriber-to-vehicle mapping heuristics 211 based on the likelihoodtime series 210. Furthermore, in some instances, additional heuristicsmay be applied to identify the start and end of subscriber paths 103.Furthermore, processing node 106 may be configured to optimize theresults by filtering out unrealistic transportation scenarios, which mayinclude, for example, frequent transfers between parallel lines on thesame itinerary, walking between two subsequent stops instead of directtransfer between connecting lines, etc., given that such scenarios havean increased likelihood of error.

In addition, the processing node 106 may be configured with one or moreprocessing modules/components to identify subscribers not using publictransport, but instead using private modes of transportation, includingcars, bicycles or other transport means. These results, along with anypublic vehicle system modality determinations resulting from thesubscriber-to-vehicle heuristics 211 discussed above, may result in adatabase 212 of individual subscriber paths 103 that are tagged with oneor more modes of transportation (or “modalities”) utilized along eachpath. In some examples, paths may be broken down into two or moreshorter pieces, or “segments,” which may have a different modality thananother segment of the path 103. Accordingly, the individual pathstagged with modality 212 may include, for each path, a plurality ofsegments that are tagged with different modalities (e.g., a particularsubscriber utilizes a subway line for a first segment of a path andtransfers to a bus line for a second segment of the path). Therefore,the processing node 106 may include one or more processingmodules/components that maintain and/or output a database containingeach individual mobile subscriber path, where each entry may include oneor more of the following fields:

-   -   Anonymized subscriber ID    -   Path start (timestamp and location)    -   Path end (timestamp and location)    -   List of path segments tagged with transport means used        -   Segment start: timestamp and location (and stop ID for            public transport)        -   Segment end: timestamp and location (and stop ID for public            transport)        -   Modality: public transport/car/bicycle/etc.        -   (Line ID for public transport)        -   (Vehicle ID for public transport)        -   Confidence indicator

Furthermore, the processing node 106 may, in a long-term profilecalculation 213, optionally build and maintain long-term commutingprofiles 214, where each profile corresponds to a particular subscriber.These long term profiles can be useful outputs for public transportplanning and can also increase the accuracy of modality assignment forindividual subscriber paths.

Thus, as outlined above, the processing node 106 is configured togenerate and expose fine-grained information about transport modalityand actual vehicle/transport lines used (i.e., for a given path and/ororigin/destination pair, generally, not only an aggregate citizen numbermay be provided but also percentage shares for different transportmodalities and actual public transport line combinations used). Also,the proposed solution provides this transport modality information innear real-time allowing also the analysis of shorter term dynamicchanges in transport patterns. This enables transport optimization usecases not yet available with any previous solutions. For instance, byutilizing the information generated by the processing node 106, systemoperators can precisely track the real impact right after publictransport line changes are implemented (e.g., how many people areleaving their cars at home and switching to a newly introduced publictransport line or the opposite, switching back to cars after aninconvenient schedule modification). Also, the information allowsoperators to optimize replacement services during new construction, lineimprovements, or after unplanned service outages or accidents. Theproposed solution may not require significant investments in many casessince mobile networks are available in each city of the world and publictransport vehicle tracking systems are also more and more common inlarge cities.

Furthermore, the processing or functionality of processing node 106 maybe considered as being performed by a single instance or device or maybe divided across a plurality of single-device processing nodes 106(each of which are configured to perform all or a portion of the exampleprocessing node 106 embodiments described herein) that may be presentand in communication in a given network/environment, such that togetherthe device instances perform all of the above-disclosed functionality.In addition, processing node 106 may be any known type of device orlogical entity associated with a communication network, wirelesscommunication network, wireline communication network, processing deviceexternal to a network infrastructure, a single processor, core, logicalor software module/component, radio communication network, or contentdelivery network, generally, that is known to perform a given disclosedprocesses or functions thereof. Examples of such processing nodesinclude eNBs (or other types of base stations or access points),Mobility Management Entities (MMEs), gateways, servers, and the like.Furthermore, in some instances, processing node 106 may represent acloud-based processing system or sub-system that employs one or morededicated or dynamically allocated processing node 106 instances, whichmay be processing devices, processors, processing cores, virtualmachines, memories, servers, and/or any other device utilized in networksystems or sub-systems configured to perform logical operations toexecute the aspects of method 300 above, or a combination of any ofthese potential processing node types.

In view of the modifications and variations described above, FIG. 3presents an example method 300 (and variations therefrom) for real-timeor near-real-time transport modality statistics by correlating mobilenetwork and service provider location data for subscribers with vehiclelocation data. For instance, at block 302 of method 300, a processingnode 106 may identify paths traveled by multiple vehicles. In an aspect,these multiple vehicles may be associated with a public transportationsystem, a private transportation service, or the like. In a furtheraspect of method 300, at block 304, the processing node 106 candetermine paths traveled by multiple subscriber telecommunicationsdevices of one or more network or service providers. In addition, atblock 306 of method 300, the processing node 106 can correlate the pathstraveled by the multiple vehicles and the paths traveled by the multiplesubscriber telecommunications devices to determine which one or moremodes of transportation each subscriber telecommunications device usedover the path traveled by the subscriber telecommunications device.

Furthermore, although not shown in FIG. 3, method 300 may includefurther aspects, including but not limited to those disclosed in one ormore of the enumerated embodiments below, which include one or more ofthe following further additional and/or alternative aspects. Forinstance, in some examples, determining the paths traveled by multiplesubscriber telecommunications devices at block 304 comprises obtaining,for each subscriber telecommunications device of the multiple subscribertelecommunications devices, location information that is obtained by theone or more network or service providers continuously and/or at regularintervals (i.e., periodically). In some examples, the paths traveled bythe multiple vehicles are identified based on positioning information(i.e., position and time information 108 of FIG. 1) obtained from apublic transport system, private transport/transportation system, or oneor more individual vehicles.

In addition, in some examples, correlating the paths at block 306 mayinclude generating a probabilistic distribution function of positions ofthe multiple subscriber telecommunications devices for eachtelecommunication cell and/or each telecommunication cell pair betweenwhich cell changes occur in the one or more network provider givenaccurate location information provided by mobile terminals. In someexamples, correlating the paths may alternatively or additionallyinclude calculating a likelihood that each of the multiple subscribertelecommunications devices 102 is riding in one of the multiple vehiclesat one or more points in time. In an additional non-limiting example,calculating the likelihood can include interpolating the location of thevehicles at the one or more points in time when subscribertelecommunication device locations are available and determining theprobability that a given subscriber telecommunications device is at asame location as a given vehicle at the one or more points in time.

In further example embodiments, method 300 may include the processingnode 106 generating multiple probability time series for multiplecombinations of subscriber telecommunications device and vehicle pairsbased on the probabilities that the given subscriber telecommunicationsdevice is at a same location as the given vehicle at the one or morepoints in time. One or more examples may additionally or alternativelyinclude identifying, from the probability time series for eachsubscriber telecommunications device, the one or more most probablevehicle line segments utilized by the subscriber associated with atleast one of the one or more subscriber telecommunications devices. Insome examples, the one or more most probable vehicle line segmentsutilized by the subscriber telecommunication devices are selected amongthe multiple probability time series based on public transport vehiclestop location data and/or are selected among the multiple probabilitytime series based on one or more maximum likelihood methods.

In some examples of method 300, the selection of the one or more mostprobable vehicle line segments utilized by the subscribertelecommunication devices among the multiple probability time series canfurther include identifying a start and an end of paths of the one ormore subscriber telecommunications devices, filtering one or more pathsegments that fall below a likelihood threshold, and/or identifying oneor more path segments for subscriber telecommunications devices wherethey are likely traveling via non-public transport vehicles.

Moreover, additional or alternative examples of method 300 includegenerating a database comprising entries for each path of each of thesubscriber telecommunications devices. In these examples, each of theentries can comprise one or more of a subscriber identifier, a pathstarting location, a path starting time, a path ending location, a pathending time, and/or a list of path segments. In some examples, thedatabase comprises a corresponding transport modality for each of thepath segments. In alternative or additional examples, each of the pathsegments include one or more of a segment starting location, a segmentstarting time, a segment starting stop, a segment ending location, asegment ending time, a segment ending stop, a mode of transportationused for the segment, a vehicle line identifier, a vehicle identifier;and/or one or more confidence indicators for one or more fields of aparticular entry.

In addition, method 300 can additionally or alternatively includegenerating a mobility profile for each of the subscribertelecommunications devices using the database. Certain embodiments mayfurther include generating a probabilistic usage profile associated withat least one of the multiple vehicles and/or public transport lines.Method 300 may further include generating, from the traffic demand,additional traffic demands (e.g., drilling down the traffic demand intosub-traffic demands) for each origin-destination pair per transportmodality.

Note that the processing node 106 as described above may performprocessing to perform any of the above aspects by implementing anyfunctional means or units. In one embodiment, for example, theprocessing node 106 comprises respective circuits configured to performthe steps of method 300 shown in FIG. 3 as well as the alternative oradditional steps/aspect outlined above. The circuits in this regard maycomprise circuits dedicated to performing certain functional processingand/or one or more microprocessors in conjunction with memory. Inembodiments that employ memory, which may comprise one or several typesof memory such as read-only memory (ROM), random-access memory, cachememory, flash memory devices, optical storage devices, etc., the memorystores program code that, when executed by the one or moremicroprocessors, carries out the techniques described herein.

FIG. 4A illustrates additional details of a processing node 106 inaccordance with one or more embodiments. As shown, the processing node106 includes one or more processing circuits 420 and, optionally, caninclude one or more radio circuits 410. The one or more radio circuits410 are configured to transmit via one or more antennas 440. The one ormore processing circuits 420 are configured to perform processingdescribed above, e.g., in FIGS. 1-3, such as by executing instructionsstored in memory 430.

FIG. 4B illustrates further aspects of the one or more processingcircuits 420, which may implement certain functional means or unitscorresponding to aspects of method 300 described above. Note thatalthough FIG. 4B illustrates three particular modules/units, they arenot exclusive or limiting. Instead, the one or more processing circuits420 may include additional modules/units or other processing circuits toperform any of the aspects of the disclosure introduced above. As shown,the processing circuit(s) 420 may implement an identifying module/unit470 for identifying paths traveled by multiple vehicles, e.g., asdescribed above for block 302 of method 300. Additionally, processingcircuits 420 may include a determining module/unit 480 for determiningpaths traveled by multiple subscriber telecommunications devices of oneor more network or service providers, e.g., as described above for block304 of method 300. In addition, processing circuits 420 can include acorrelating module/unit 490 for correlating the paths traveled by themultiple vehicles and the paths traveled by the multiple subscribertelecommunications devices to determine which one or more modes oftransportation each subscriber telecommunications device used over thepath traveled by the subscriber telecommunications device, e.g., asdescribed above for block 306 of method 300.

Additional details of the processing node 106 are shown in relation toFIG. 5. As shown in FIG. 5, the example processing node 106 includes anantenna 540, radio circuitry (e.g., radio front-end circuitry) 510,processing circuitry 520, and the processing node 106 may also include amemory 530. The processing circuitry 520, which may correspond to theone or more processing circuits 420 of FIGS. 4A and/or 4B, may beconfigured to perform any of the aspects of method 300 of FIG. 3discussed above, and any other aspects discussed herein, generally. Thememory 530 may be separate from the processing circuitry 520 or anintegral part of processing circuitry 520. Antenna 540 may include oneor more antennas or antenna arrays, and is configured to send and/orreceive wireless signals, and is connected to radio circuitry (e.g.,radio front-end circuitry) 510. Where processing node 106 does notinclude wireless communication capability, or even where it does, theprocessing node 106 includes communication circuitry for communicationover one or more wires, transmission lines, busses, or other physicalmedia. In certain alternative embodiments, processing node 106 may notinclude antenna 540, and antenna 540 may instead be separate fromprocessing node 106 and be connectable to processing node 106 through aninterface or port.

The radio circuitry (e.g., radio front-end circuitry) 510 may comprisevarious filters and amplifiers, is connected to antenna 540 andprocessing circuitry 520, and is configured to condition signalscommunicated between antenna 540 and processing circuitry 520. Incertain alternative embodiments, processing node 106 may not includeradio circuitry (e.g., radio front-end circuitry) 510, and processingcircuitry 520 may instead be connected to antenna 540 without front-endcircuitry 510.

Processing circuitry 520 may include one or more of radio frequency (RF)transceiver circuitry, baseband processing circuitry, and applicationprocessing circuitry. In some embodiments, the RF transceiver circuitry521, baseband processing circuitry 522, and application processingcircuitry 523 may be on separate chipsets. In alternative embodiments,part or all of the baseband processing circuitry 522 and applicationprocessing circuitry 523 may be combined into one chipset, and the RFtransceiver circuitry 521 may be on a separate chipset. In stillalternative embodiments, part or all of the RF transceiver circuitry 521and baseband processing circuitry 522 may be on the same chipset, andthe application processing circuitry 523 may be on a separate chipset.In yet other alternative embodiments, part or all of the RF transceivercircuitry 521, baseband processing circuitry 522, and applicationprocessing circuitry 523 may be combined in the same chipset. Processingcircuitry 520 may include, for example, one or more central processingunits (CPUs), one or more microprocessors, one or more applicationspecific integrated circuits (ASICs), and/or one or more fieldprogrammable gate arrays (FPGAs).

The processing node 106 may include a power source 550. The power source550 may be a battery or other power supply circuitry, includingwired/dedicated power, as well as power management circuitry. The powersupply circuitry may receive power from an external source. A battery,other power supply circuitry, and/or power management circuitry areconnected to radio circuitry (e.g., radio front-end circuitry) 510,processing circuitry 520, and/or memory 530. The power source 550,battery, power supply circuitry, and/or power management circuitry areconfigured to supply processing node 106, including processing circuitry520, with power for performing the functionality described herein.

In the following, test results of exemplary tests which have beenperformed using the technique presented herein are described withreference to FIGS. 6a to 6d . The tests have been performed on live datain a larger city to validate the feasibility of the proposed concept. Inthese tests, a user has queried two Android test terminals. In the firstterminal, a custom-built Android app collected location information ofnearby public transport vehicles using an HTTP-based API that theofficial app of the public transport company in this city used. Inparallel, for the other test terminal, location data was collected fromthe mobile network. The second test terminal was occasionally used forchecking web sites but was also idle for longer periods.

The diagrams of FIGS. 6a to 6d visualize different processing steps inthe prototype implementation for a circular test path in the larger city(note: actual lat/lon coordinates are offset in order not to reveal theidentity of the public transport company and the mobile network). Amongthe diagrams, FIG. 6a shows recorded locations of public transportvehicles in the proximity of the test user, FIG. 6b shows recordedlocations of the test user from the mobile network, FIG. 6c shows amapping of the test user to public transport vehicles, and FIG. 6d showsthe final results of the proposed technique.

In a further consideration of the technique presented herein, it isnoted that using location data of individuals from telecom networks mayhave privacy aspects. FIG. 7 therefore illustrates a multi-layeranonymization schema which may be used to protect the privacy of allindividuals.

Still further, it is noted that the proposed system may involveprocessing of huge amounts of mobile network data, typically frommillions of mobile network subscribers. In contrast, vehicle locationdata may be orders of magnitudes smaller, typically covering thousandsof vehicles. This may allow scaling options in a cloud environment:subscriber location data from mobile networks can be load balanced basedon a hash of an anonymized subscriber ID and processed independentlyfrom each other in separate instances while vehicle location data can besimply replicated towards all individual instances (much smallervolume). Such approach is schematically illustrated in FIG. 8.

Though not explicitly described above, the present disclosure envisionsfurther example embodiments that may be related to aspects of theabove-described embodiments. As such, the above-described embodimentsare not limiting. What is more, additional or alternative embodimentsassociated with or performed by the processing node 106 or subscribertelecommunications device 102 may be utilized in some implementations.

Those skilled in the art will also appreciate that embodiments hereinfurther include corresponding computer programs. A computer programcomprises instructions which, when executed on at least one processor ofa node, cause the node to carry out any of the respective processingdescribed above. A computer program in this regard may comprise one ormore code modules corresponding to the means or units described above.Embodiments further include a carrier containing such a computerprogram. This carrier may comprise one of an electronic signal, opticalsignal, radio signal, or computer readable storage medium.

Those skilled in the art will recognize that the present invention maybe carried out in other ways than those specifically set forth hereinwithout departing from essential characteristics of the invention. Thepresent embodiments are thus to be considered in all respects asillustrative and not restrictive, and all changes coming within themeaning and equivalency range of the appended claims are intended to beembraced therein.

1-24. (canceled)
 25. A method, comprising: identifying paths traveled bymultiple vehicles; determining paths traveled by multiple subscribertelecommunications devices of one or more network or service providers;and correlating the paths traveled by the multiple vehicles and thepaths traveled by the multiple subscriber telecommunications devices todetermine which one or more modes of transportation each subscribertelecommunications device used over the path traveled by the subscribertelecommunications device.
 26. The method of claim 25, wherein thedetermining the paths traveled by multiple subscriber telecommunicationsdevices comprises obtaining, for each subscriber telecommunicationsdevice of the multiple subscriber telecommunications devices, locationinformation that is obtained by the one or more network or serviceproviders continuously and/or at regular intervals.
 27. The method ofclaim 25, wherein the paths traveled by the multiple vehicles areidentified based on positioning information obtained from a publictransport system.
 28. The method of claim 25, wherein the correlatingthe paths comprises generating a probabilistic distribution function ofpositions of the multiple subscriber telecommunications devices for eachtelecommunication cell and/or each telecommunication cell pair betweenwhich cell changes occur in the one or more network providers givenaccurate location information provided by mobile terminals.
 29. Themethod of claim 25, wherein the correlating the paths comprisescalculating a likelihood that each of the multiple subscribertelecommunications devices is riding in one of the multiple vehicles atone or more points in time.
 30. The method of claim 29, wherein thecalculating the likelihood comprises: interpolating the location of thevehicles at the one or more points in time when subscribertelecommunication device locations are available; and determining theprobability that a given subscriber telecommunications device is at asame location as a given vehicle at the one or more points in time. 31.The method of claim 29, further comprising generating multipleprobability time series for multiple combinations of subscribertelecommunications device and vehicle pairs based on the probabilitiesthat the given subscriber telecommunications device is at a samelocation as the given vehicle at the one or more points in time.
 32. Themethod of claim 31, further comprising identifying, from the probabilitytime series for each subscriber telecommunications device, one or moremost probable vehicle line segments utilized by the subscriberassociated with at least one of the one or more subscribertelecommunications devices.
 33. The method of claim 32, wherein the oneor more most probable vehicle line segments utilized by the subscribertelecommunication devices are selected among the multiple probabilitytime series based on public transport vehicle stop location data. 34.The method of claim 32, wherein the one or more most probable vehicleline segments utilized by the subscriber telecommunication devices areselected among the multiple probability time series based on one or moremaximum likelihood methods.
 35. The method of claim 33, wherein theselection of the one or more most probable vehicle line segmentsutilized by the subscriber telecommunication devices among the multipleprobability time series comprises: identifying a start and an end ofpaths of the one or more subscriber telecommunications devices;filtering one or more path segments that fall below a likelihoodthreshold; and/or identifying one or more path segments for subscribertelecommunications devices where they are likely traveling vianon-public transport vehicles.
 36. The method of claim 25, furthercomprising generating a database comprising entries for each path ofeach of the subscriber telecommunications devices, wherein each of theentries comprises: a subscriber identifier; a path starting location; apath starting time; a path ending location; a path ending time; and/or alist of path segments.
 37. The method of claim 36, wherein the databasecomprises, for each of the path segments, a corresponding transportmodality.
 38. The method of claim 37, wherein the database comprises,for each of the path segments: a segment starting location; a segmentstarting time; a segment starting stop; a segment ending location; asegment ending time; a segment ending stop; a mode of transportationused for the segment; a vehicle line identifier; a vehicle identifier;and/or one or more confidence indicators for one or more fields of aparticular entry.
 39. The method of claim 36, further comprisinggenerating a mobility profile for each of the subscribertelecommunications devices using the database.
 40. The method of claim36, further comprising generating a probabilistic usage profileassociated with at least one of the multiple vehicles or publictransport lines.
 41. The method of claim 36, further comprisinggenerating an origin-destination matrix indicating traffic demand as afunction of time.
 42. The method of claim 41, further comprisinggenerating, from the traffic demand, additional traffic demands for eachorigin-destination pair per transport modality.
 43. A processing node,comprising: processing circuitry; memory containing instructionsexecutable by the processing circuitry whereby the processing node isoperative to: identify paths traveled by multiple vehicles; determinepaths traveled by multiple subscriber telecommunications devices of oneor more network or service providers; and correlate the paths traveledby the multiple vehicles and the paths traveled by the multiplesubscriber telecommunications devices to determine which one or moremodes of transportation each subscriber telecommunications device usedover the path traveled by the subscriber telecommunications device. 44.A non-transitory computer readable recording medium storing a computerprogram product for controlling a processing node, the computer programproduct comprising software instructions which, when run on processingcircuitry of the processing node, causes the processing node to:identify paths traveled by multiple vehicles; determine paths traveledby multiple subscriber telecommunications devices of one or more networkor service providers; and correlate the paths traveled by the multiplevehicles and the paths traveled by the multiple subscribertelecommunications devices to determine which one or more modes oftransportation each subscriber telecommunications device used over thepath traveled by the subscriber telecommunications device.